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A Non-parametric Bayesian Method for Hierarchical Clustering of Longitudinal Data

Ren, Yan
http://rave.ohiolink.edu/etdc/view?acc_num=ucin1337085531

Abstract Details

2012, PhD, University of Cincinnati, Arts and Sciences: Mathematical Sciences.
In longitudinal studies, we are often interested in simultaneously clustering observations at both subject- and time-levels. Current clustering approaches assume the exchangeability among clustering units, and they are not applicable for our clustering goal. Through the use of a specific base measure, we propose a more suitable method that improves upon the multivariate DP mixture model. A well-known MCMC algorithm, Gibbs sampler, is implemented for the Bayesian posterior distributions and estimates. We compare two kinds of specific base measures from simple to complex. The models are evaluated through simulation studies of multivariate data with different covariance specifications. Performance is assessed by the stationarity, the autocorrelation functions of the Markov chain, the correct classification rates, the 95% credible intervals for parameter estimates, and the CPU time. We illustrate the method with data from a prospective longitudinal study on sleep apnea, tracking the diastolic blood pressure and severity of sleep apnea of 97 children during 24 hours.
Siva Sivaganesan, PhD (Committee Chair)
Mekibib Altaye, PhD (Committee Member)
James Deddens, PhD (Committee Member)
Paul Horn, PhD (Committee Member)
Seongho Song, PhD (Committee Member)
Rhonda VanDyke, PhD (Committee Member)
181 p.
Statistics
cluster analysis; Bayesian; longitudinal data; Dirichlet process mixture (DPM) model; reversible jump MCMC; Gibbs;

Recommended Citations

Citations

  • Ren, Y. (2012). A Non-parametric Bayesian Method for Hierarchical Clustering of Longitudinal Data [Doctoral dissertation, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1337085531

    APA Style (7th edition)

  • Ren, Yan. A Non-parametric Bayesian Method for Hierarchical Clustering of Longitudinal Data. 2012. University of Cincinnati, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ucin1337085531.

    MLA Style (8th edition)

  • Ren, Yan. "A Non-parametric Bayesian Method for Hierarchical Clustering of Longitudinal Data." Doctoral dissertation, University of Cincinnati, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1337085531

    Chicago Manual of Style (17th edition)

ucin1337085531
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© 2012, all rights reserved.
This open access ETD is published by University of Cincinnati and OhioLINK.